Skip to content

GitLab

Commit 675baa79 authored by Tim Dettmers's avatar Tim Dettmers
Browse files

Merge remote-tracking branch 'origin/main' into merge

parents f64cfe65 9e7cdc9e
Hide whitespace changes
Inline Side-by-side
Showing with 1418 additions and 44 deletions
bitsandbytes/research/__init__.py 0 → 100644
View file @ 675baa79
from . import nn
from .autograd._functions import (
switchback_bnb,
matmul_fp8_global,
matmul_fp8_mixed,
)
bitsandbytes/research/autograd/_functions.py 0 → 100644
View file @ 675baa79
import operator
import warnings
from dataclasses import dataclass
from functools import reduce # Required in Python 3
import torch
import bitsandbytes.functional as F
from bitsandbytes.autograd._functions import MatmulLtState, GlobalOutlierPooler
# math.prod not compatible with python < 3.8
def prod(iterable):
return reduce(operator.mul, iterable, 1)
tensor = torch.Tensor
class MatMulFP8Mixed(torch.autograd.Function):
# forward is the same, but we added the fallback for pre-turing GPUs
# backward is mostly the same, but adds one extra clause (see "elif state.CxB is not None")
@staticmethod
def forward(ctx, A, B, out=None, fw_code=None, bw_code=None, bsz=1024, bsz2=1024):
# default of pytorch behavior if inputs are empty
ctx.is_empty = False
if prod(A.shape) == 0:
ctx.is_empty = True
ctx.A = A
ctx.B = B
B_shape = B.shape
if A.shape[-1] == B_shape[0]:
return torch.empty(A.shape[:-1] + B_shape[1:], dtype=A.dtype, device=A.device)
else:
return torch.empty(A.shape[:-1] + B_shape[:1], dtype=A.dtype, device=A.device)
# 1. Dequantize
# 2. MatmulnN
cA, state = F.quantize_blockwise(A, code=fw_code, blocksize=bsz)
fp8A = F.dequantize_blockwise(cA, state, blocksize=bsz).to(A.dtype)
cB, state = F.quantize(B.float(), code=fw_code)
fp8B = F.dequantize(cB, state).to(B.dtype)
output = torch.matmul(fp8A, fp8B)
# output is half
# 3. Save state
ctx.fw_code = fw_code
ctx.bw_code = bw_code
ctx.bsz = bsz
ctx.bsz2 = bsz2
ctx.dtype_A, ctx.dtype_B = A.dtype, B.dtype
if any(ctx.needs_input_grad[:2]):
# NOTE: we send back A, and re-quant.
ctx.tensors = (A, fp8B)
else:
ctx.tensors = (None, None)
return output
@staticmethod
def backward(ctx, grad_output):
if ctx.is_empty:
return torch.zeros_like(ctx.A), torch.zeros_like(ctx.B), None, None, None, None, None
req_gradA, req_gradB, _, _, _, _, _ = ctx.needs_input_grad
A, B = ctx.tensors
grad_A, grad_B = None, None
# TODO: Fix blocksize to be output_dim
cgrad_out, state = F.quantize_blockwise(grad_output, code=ctx.bw_code, blocksize=ctx.bsz2)
fp8out = F.dequantize_blockwise(cgrad_out, state, blocksize=ctx.bsz2).to(grad_output.dtype)
# cgrad_output_2, state_2 = F.quantize(grad_output.float(), code=ctx.bw_code)
# fp8out_2 = F.dequantize(cgrad_output_2, state_2).to(grad_output.dtype)
# grad_output_reshape = grad_output.reshape(-1, grad_output.shape[-1]).contiguous()
# fp8grad_transpose, stategrad_transpose = F.vectorwise_quant(grad_output_reshape, dim=0, quant_type='vector')
# fp8out_transpose = (fp8grad_transpose / 7) * stategrad_transpose
# fp8out_transpose = fp8out_transpose.view(grad_output.shape[0], grad_output.shape[1], grad_output.shape[2])
# not supported by PyTorch. TODO: create work-around
if req_gradA:
grad_A = torch.matmul(fp8out, B.t().to(fp8out.dtype)).to(A.dtype)
if req_gradB:
if len(A.shape) == 3:
At = A.transpose(2, 1).contiguous()
else:
At = A.transpose(1, 0).contiguous()
# cA, state = F.quantize(At.float(), code=ctx.fw_code)
# fp8At = F.dequantize(cA, state).to(A.dtype)
grad_B = torch.matmul(At.to(grad_output.dtype), grad_output).to(B.dtype)
return grad_A, grad_B, None, None, None, None, None
class MatMulFP8Global(torch.autograd.Function):
# forward is the same, but we added the fallback for pre-turing GPUs
# backward is mostly the same, but adds one extra clause (see "elif state.CxB is not None")
@staticmethod
def forward(ctx, A, B, out=None, fw_code=None, bw_code=None, bsz=1024, bsz2=1024):
# default of pytorch behavior if inputs are empty
ctx.is_empty = False
if prod(A.shape) == 0:
ctx.is_empty = True
ctx.A = A
ctx.B = B
B_shape = B.shape
if A.shape[-1] == B_shape[0]:
return torch.empty(A.shape[:-1] + B_shape[1:], dtype=A.dtype, device=A.device)
else:
return torch.empty(A.shape[:-1] + B_shape[:1], dtype=A.dtype, device=A.device)
# 1. Dequantize
# 2. MatmulnN
cA, state = F.quantize(A.float(), code=fw_code)
fp8A = F.dequantize(cA, state).to(A.dtype)
cB, state = F.quantize(B.float(), code=fw_code)
fp8B = F.dequantize(cB, state).to(B.dtype)
output = torch.matmul(fp8A, fp8B)
# output is half
# 3. Save state
ctx.fw_code = fw_code
ctx.bw_code = bw_code
ctx.bsz = bsz
ctx.bsz2 = bsz2
ctx.dtype_A, ctx.dtype_B = A.dtype, B.dtype
if any(ctx.needs_input_grad[:2]):
# NOTE: we send back A, and re-quant.
ctx.tensors = (A, fp8B)
else:
ctx.tensors = (None, None)
return output
@staticmethod
def backward(ctx, grad_output):
if ctx.is_empty:
return torch.zeros_like(ctx.A), torch.zeros_like(ctx.B), None, None, None, None, None
req_gradA, req_gradB, _, _, _, _, _ = ctx.needs_input_grad
A, B = ctx.tensors
grad_A, grad_B = None, None
# TODO: Fix blocksize to be output_dim
cgrad_out, state = F.quantize(grad_output.float(), code=ctx.bw_code)
fp8out = F.dequantize(cgrad_out, state).to(grad_output.dtype)
# cgrad_output_2, state_2 = F.quantize(grad_output.float(), code=ctx.bw_code)
# fp8out_2 = F.dequantize(cgrad_output_2, state_2).to(grad_output.dtype)
# grad_output_reshape = grad_output.reshape(-1, grad_output.shape[-1]).contiguous()
# fp8grad_transpose, stategrad_transpose = F.vectorwise_quant(grad_output_reshape, dim=0, quant_type='vector')
# fp8out_transpose = (fp8grad_transpose / 7) * stategrad_transpose
# fp8out_transpose = fp8out_transpose.view(grad_output.shape[0], grad_output.shape[1], grad_output.shape[2])
# not supported by PyTorch. TODO: create work-around
if req_gradA:
grad_A = torch.matmul(fp8out, B.t().to(fp8out.dtype)).to(A.dtype)
if req_gradB:
if len(A.shape) == 3:
At = A.transpose(2, 1).contiguous()
else:
At = A.transpose(1, 0).contiguous()
cA, state = F.quantize(At.float(), code=ctx.fw_code)
fp8At = F.dequantize(cA, state).to(A.dtype)
grad_B = torch.matmul(fp8At.to(fp8out.dtype), fp8out).to(B.dtype)
return grad_A, grad_B, None, None, None, None, None
class SwitchBackBnb(torch.autograd.Function):
@staticmethod
def forward(ctx, A, B, out=None, bias=None, state=MatmulLtState()):
# default to pytorch behavior if inputs are empty
ctx.is_empty = False
if prod(A.shape) == 0:
ctx.is_empty = True
ctx.A = A
ctx.B = B
ctx.bias = bias
if A.shape[-1] == B.shape[0]:
return torch.empty(A.shape[:-1]+B.shape[1:], dtype=A.dtype, device=A.device)
else:
return torch.empty(A.shape[:-1]+B.shape[:1], dtype=A.dtype, device=A.device)
# 1. Quantize A
# 2. Quantize B
# 3. Matmul
# 4. Mixed-precision decomposition matmul
# 5. Save state
formatB = state.formatB
input_shape = A.shape
if state.outlier_pool is None:
state.outlier_pool = GlobalOutlierPooler.get_instance()
# Cast A to fp16
if A.dtype != torch.float16:
warnings.warn(f"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization")
# 1. Quantize A
if len(A.shape) == 3:
A = A.view(-1, A.shape[-1]).contiguous()
CA, CAt, SCA, SCAt, coo_tensorA = F.double_quant(
A.to(torch.float16), threshold=state.threshold
)
if state.threshold > 0.0 and coo_tensorA is not None:
if state.has_fp16_weights:
idx = torch.unique(coo_tensorA.colidx).long()
CA[:, idx] = 0
CAt[:, idx] = 0
subA = A[:, idx]
state.subB = B[:, idx].t().contiguous()
state.idx = idx
else:
if state.CxB is None:
# B in in 8-bit row-major, we can transform it back to 16-bit to extract outlier dimensions
# we also need to convert it to the turing/ampere format
state.CxB, state.SB = F.transform(state.CB, to_order=formatB)
else:
#print('A shape', A.shape)
if not state.has_fp16_weights and state.CxB is None:
state.CxB, state.SB = F.transform(state.CB, to_order=formatB)
subA = None
# 2. Quantize B
if state.has_fp16_weights:
#print('B shape', B.shape)
has_grad = True if (getattr(B, "grad", None) is not None) else False
is_transposed = not B.is_contiguous() and B.shape[0] == B.stride(1)
if is_transposed:
B = B.contiguous()
if (state.is_training and not has_grad) or state.CxB is None:
state.reset_grads()
(
CB,
state.CBt,
state.SCB,
state.SCBt,
coo_tensorB,
) = F.double_quant(B.to(torch.float16))
state.CxB, state.SB = F.transform(CB, to_order=formatB)
else:
has_grad = False
if coo_tensorA is not None and not state.has_fp16_weights:
# extract outliers
outlier_idx = torch.unique(coo_tensorA.colidx)
state.idx = outlier_idx
# state.outlier_pool.add_outliers(outlier_idx, A.shape[-1])
# if state.use_pool and state.outlier_pool.model_dim == A.shape[-1]:
# # do not use pool for 2nd FFN layer
# state.idx = state.outlier_pool.get_current_outlier_idx().to(A.device)
# else:
# state.idx = outlier_idx
outliers = F.extract_outliers(state.CxB, state.SB, state.idx.int())
state.subB = (
(outliers * state.SCB.view(-1, 1) / 127.0)
.t()
.contiguous()
.to(A.dtype)
)
CA[:, state.idx.long()] = 0
CAt[:, state.idx.long()] = 0
subA = A[:, state.idx.long()]
shapeB = state.SB[0]
if len(input_shape) == 3:
output_shape = (input_shape[0], input_shape[1], shapeB[0])
else:
output_shape = (input_shape[0], shapeB[0])
# 3. Matmul
C32A, SA = F.transform(CA, "col32")
out32, Sout32 = F.igemmlt(C32A, state.CxB, SA, state.SB)
# we apply the fused bias here
if bias is None or bias.dtype == torch.float16:
output = F.mm_dequant(out32, Sout32, SCA, state.SCB, bias=bias)
output = output.to(A.dtype)
else: # apply bias separately
output = F.mm_dequant(out32, Sout32, SCA, state.SCB, bias=None)
output = output.to(A.dtype).add_(bias)
# 4. Mixed-precision decomposition matmul
if coo_tensorA is not None and subA is not None:
output += torch.matmul(subA, state.subB)
# 5. Save state
ctx.state = state
ctx.formatB = formatB
ctx.grad_shape = input_shape
ctx.dtype_A, ctx.dtype_B, ctx.dtype_bias = A.dtype, B.dtype, None if bias is None else bias.dtype
if any(ctx.needs_input_grad[:2]):
ctx.tensors = (CAt, subA, A)
ctx.tensor_states = (SCAt, state.idx)
else:
ctx.tensors = [None, None, None]
ctx.tensor_states = (None, None)
ctx.save_for_backward(None, None)
clone_func = torch.clone if len(output_shape) == 3 else lambda x : x
return clone_func(output.view(output_shape))
@staticmethod
def backward(ctx, grad_output):
if ctx.is_empty:
bias_grad = (None if ctx.bias is None else torch.zeros_like(ctx.bias))
return torch.zeros_like(ctx.A), torch.zeros_like(ctx.B), None, bias_grad, None
req_gradA, req_gradB, _, req_gradBias, _ = ctx.needs_input_grad
CAt, subA, A = ctx.tensors
SCAt, idx = ctx.tensor_states
formatB = ctx.formatB
state = ctx.state
grad_A = grad_B = grad_bias = None
if req_gradBias:
# compute grad_bias first before changing grad_output dtype
grad_bias = grad_output.sum(0, dtype=ctx.dtype_bias)
# Cast grad_output to fp16
if len(grad_output.shape) == 3:
grad_output = grad_output.reshape(
-1, grad_output.shape[-1]
).contiguous()
Cgrad, Cgradt, SCgrad, SCgradt, coo_tensor = F.double_quant(grad_output.to(torch.float16))
if req_gradB:
# print('back A shape', A.shape)
# print('grad output t shape', grad_output.t().shape)
grad_B = torch.matmul(grad_output.t(), A)
if req_gradA:
if state.CBt is not None:
C32grad, Sgrad = F.transform(Cgrad, "col32")
if state.CxBt is None:
state.CxBt, state.SBt = F.transform(
state.CBt, to_order=formatB, transpose=True
)
# print('back B shape', state.CxBt.shape)
# print('back grad shape', C32grad.shape)
gradA32, SgradA32 = F.igemmlt(C32grad, state.CxBt, Sgrad, state.SBt)
grad_A = F.mm_dequant(gradA32, SgradA32, SCgrad, state.SCBt).view(ctx.grad_shape).to(ctx.dtype_A)
elif state.CB is not None:
CB = state.CB.to(ctx.dtype_A, copy=True).mul_(state.SCB.unsqueeze(1).mul(1. / 127.0))
grad_A = torch.matmul(grad_output, CB).view(ctx.grad_shape).to(ctx.dtype_A)
else:
raise Exception('State must contain either CBt or CB matrix for backward')
return grad_A, grad_B, None, grad_bias, None
def get_block_sizes(input_matrix, weight_matrix):
input_features = input_matrix.shape[-1]
output_features = (weight_matrix.shape[0] if weight_matrix.shape[1] == input_features else weight_matrix.shape[1])
array = [4096, 2048, 1024, 512, 256, 128, 64, 0]
bsz, bsz2 = 1024, 1024
for i, k in enumerate(array):
if input_features > array[i + 1]:
bsz = k
break
for i, k in enumerate(array):
if output_features > array[i + 1]:
bsz2 = k
break
return bsz, bsz2
def matmul_fp8_global(A: tensor, B: tensor, fw_code: tensor, bw_code: tensor, out: tensor = None, bsz : int = -1, bsz2 : int = -1):
if bsz == -1 or bsz2 == -1: bsz, bsz2 = get_block_sizes(A, B)
return MatMulFP8Global.apply(A, B, out, fw_code, bw_code, bsz, bsz2)
def matmul_fp8_mixed(A: tensor, B: tensor, fw_code: tensor, bw_code: tensor, out: tensor = None, bsz : int = -1, bsz2 : int = -1):
if bsz == -1 or bsz2 == -1: bsz, bsz2 = get_block_sizes(A, B)
return MatMulFP8Mixed.apply(A, B, out, fw_code, bw_code, bsz, bsz2)
def switchback_bnb(
A: tensor,
B: tensor,
out: tensor = None,
state: MatmulLtState = None,
threshold=0.0,
bias=None
):
state = state or MatmulLtState()
if threshold > 0.0:
state.threshold = threshold
return SwitchBackBnb.apply(A, B, out, bias, state)
bitsandbytes/research/nn/__init__.py 0 → 100644
View file @ 675baa79
from .modules import LinearFP8Mixed, LinearFP8Global
bitsandbytes/research/nn/modules.py 0 → 100644
View file @ 675baa79
from typing import Optional, TypeVar, Union, overload
import torch
import torch.nn.functional as F
from torch import Tensor, device, dtype, nn
import bitsandbytes as bnb
from bitsandbytes.optim import GlobalOptimManager
from bitsandbytes.utils import OutlierTracer, find_outlier_dims
T = TypeVar("T", bound="torch.nn.Module")
class LinearFP8Mixed(nn.Linear):
def __init__(self, input_features, output_features, bias=True):
super().__init__(input_features, output_features, bias)
self.bw_code = None
self.fw_code = None
array = [4096, 2048, 1024, 512, 256, 128, 64, 0]
for i, k in enumerate(array):
if input_features > array[i + 1]:
self.bsz = k
break
for i, k in enumerate(array):
if output_features > array[i + 1]:
self.bsz2 = k
break
def forward(self, x: torch.Tensor):
if self.fw_code is None:
self.bw_code = bnb.functional.create_fp8_map(True, 5, 2, 8).to(x.device)
self.fw_code = bnb.functional.create_fp8_map(True, 4, 3, 8).to(x.device)
out = bnb.research.matmul_fp8_mixed(x, self.weight.t(), fw_code=self.fw_code, bw_code=self.bw_code, bsz=self.bsz, bsz2=self.bsz2)
if self.bias is not None:
out += self.bias
return out
class LinearFP8Global(nn.Linear):
def __init__(self, input_features, output_features, bias=True):
super().__init__(input_features, output_features, bias)
self.bw_code = None
self.fw_code = None
array = [4096, 2048, 1024, 512, 256, 128, 64, 0]
for i, k in enumerate(array):
if input_features > array[i + 1]:
self.bsz = k
break
for i, k in enumerate(array):
if output_features > array[i + 1]:
self.bsz2 = k
break
def forward(self, x: torch.Tensor):
if self.fw_code is None:
self.bw_code = bnb.functional.create_fp8_map(True, 5, 2, 8).to(x.device)
self.fw_code = bnb.functional.create_fp8_map(True, 4, 3, 8).to(x.device)
out = bnb.matmul_fp8_global(x, self.weight.t(), fw_code=self.fw_code, bw_code=self.bw_code, bsz=self.bsz, bsz2=self.bsz2)
if self.bias is not None:
out += self.bias
return out
bitsandbytes/triton/dequantize_rowwise.py 0 → 100644
View file @ 675baa79
import math
import torch
import time
from bitsandbytes.triton.triton_utils import is_triton_available
if not is_triton_available():
def dequantize_rowwise(x: torch.Tensor, state_x: torch.Tensor): return None
else:
import triton
import triton.language as tl
from triton.ops.matmul_perf_model import early_config_prune, estimate_matmul_time
# rowwise quantize
# TODO: autotune this better.
@triton.autotune(
configs=[
triton.Config({}, num_stages=1, num_warps=8),
triton.Config({}, num_stages=2, num_warps=8),
triton.Config({}, num_stages=4, num_warps=8),
triton.Config({}, num_stages=8, num_warps=8),
triton.Config({}, num_stages=1),
triton.Config({}, num_stages=2),
triton.Config({}, num_stages=4),
triton.Config({}, num_stages=8),
triton.Config({}, num_warps=1),
triton.Config({}, num_warps=2),
triton.Config({}, num_warps=4),
triton.Config({}, num_warps=8),
],
key=['n_elements']
)
@triton.jit
def _dequantize_rowwise(
x_ptr,
state_x,
output_ptr,
inv_127,
n_elements,
BLOCK_SIZE: tl.constexpr,
P2: tl.constexpr,
):
pid = tl.program_id(axis=0)
block_start = pid * BLOCK_SIZE
arange = tl.arange(0, P2)
offsets = block_start + arange
row_mask = arange < BLOCK_SIZE
x = tl.load(x_ptr + offsets, mask=row_mask)
max_val = tl.load(state_x + pid)
output = max_val * x * inv_127
tl.store(output_ptr + offsets, output, mask=row_mask)
def dequantize_rowwise(x: torch.Tensor, state_x: torch.Tensor):
output = torch.empty(*x.shape, device=x.device, dtype=torch.float16)
P2 = int(2 ** (math.ceil(math.log2(x.shape[1]))))
assert x.is_cuda and output.is_cuda
n_elements = output.numel()
grid = lambda meta: (x.shape[0],)
_dequantize_rowwise[grid](x, state_x, output, 1./127, n_elements, BLOCK_SIZE=x.shape[1], P2=P2)
return output
bitsandbytes/triton/int8_matmul_mixed_dequanitze.py 0 → 100644
View file @ 675baa79
import torch
from bitsandbytes.triton.triton_utils import is_triton_available
if not is_triton_available():
def int8_matmul_mixed_dequanitze(a, b, state_x, state_w, bias): return None
else:
import triton
import triton.language as tl
from triton.ops.matmul_perf_model import early_config_prune, estimate_matmul_time
# This is a matmul kernel based on triton.ops.matmul
# It is modified to support rowwise quantized input and global quantized weight
# It's purpose is fused matmul then dequantize
# It does support bias.
def init_to_zero(name):
return lambda nargs: nargs[name].zero_()
def get_configs_io_bound():
configs = []
for num_stages in [2, 3, 4, 5, 6]:
for block_m in [16, 32]:
for block_k in [32, 64]:
for block_n in [32, 64, 128, 256]:
num_warps = 2 if block_n <= 64 else 4
configs.append(
triton.Config({'BLOCK_M': block_m, 'BLOCK_N': block_n, 'BLOCK_K': block_k, 'SPLIT_K': 1},
num_stages=num_stages, num_warps=num_warps))
# split_k
for split_k in [2, 4, 8, 16]:
configs.append(triton.Config({'BLOCK_M': block_m, 'BLOCK_N': block_n, 'BLOCK_K': block_k, 'SPLIT_K': split_k},
num_stages=num_stages, num_warps=num_warps, pre_hook=init_to_zero('C')))
return configs
@triton.autotune(
configs=[
# basic configs for compute-bound matmuls
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 256, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=3, num_warps=8),
triton.Config({'BLOCK_M': 256, 'BLOCK_N': 128, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=3, num_warps=8),
triton.Config({'BLOCK_M': 256, 'BLOCK_N': 64, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_M': 64, 'BLOCK_N': 256, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 128, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 64, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_M': 64, 'BLOCK_N': 128, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 32, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_M': 64, 'BLOCK_N': 32, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=5, num_warps=2),
# good for int8
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 256, 'BLOCK_K': 128, 'SPLIT_K': 1}, num_stages=3, num_warps=8),
triton.Config({'BLOCK_M': 256, 'BLOCK_N': 128, 'BLOCK_K': 128, 'SPLIT_K': 1}, num_stages=3, num_warps=8),
triton.Config({'BLOCK_M': 256, 'BLOCK_N': 64, 'BLOCK_K': 128, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_M': 64, 'BLOCK_N': 256, 'BLOCK_K': 128, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 128, 'BLOCK_K': 128, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 64, 'BLOCK_K': 64, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_M': 64, 'BLOCK_N': 128, 'BLOCK_K': 64, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 32, 'BLOCK_K': 64, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_M': 64, 'BLOCK_N': 32, 'BLOCK_K': 64, 'SPLIT_K': 1}, num_stages=5, num_warps=2),
] + get_configs_io_bound(),
key=['M', 'N', 'K'],
prune_configs_by={
'early_config_prune': early_config_prune,
'perf_model': estimate_matmul_time,
'top_k': 10
},
)
@triton.heuristics({
'EVEN_K': lambda args: args['K'] % (args['BLOCK_K'] * args['SPLIT_K']) == 0,
})
@triton.jit
def _int8_matmul_mixed_dequantize(A, B, C, bias, state_x_ptr, state_w_ptr, M, N, K, divfactor: tl.constexpr, has_bias : tl.constexpr,
stride_am, stride_ak,
stride_bk, stride_bn,
stride_cm, stride_cn,
BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr,
GROUP_M: tl.constexpr, SPLIT_K: tl.constexpr, EVEN_K: tl.constexpr,
ACC_TYPE: tl.constexpr
):
# matrix multiplication
pid = tl.program_id(0)
pid_z = tl.program_id(1)
grid_m = tl.cdiv(M, BLOCK_M)
grid_n = tl.cdiv(N, BLOCK_N)
# re-order program ID for better L2 performance
width = GROUP_M * grid_n
group_id = pid // width
group_size = min(grid_m - group_id * GROUP_M, GROUP_M)
pid_m = group_id * GROUP_M + (pid % group_size)
pid_n = (pid % width) // (group_size)
# do matrix multiplication
rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
rn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
ram = tl.max_contiguous(tl.multiple_of(rm % M, BLOCK_M), BLOCK_M)
rbn = tl.max_contiguous(tl.multiple_of(rn % N, BLOCK_N), BLOCK_N)
rk = pid_z * BLOCK_K + tl.arange(0, BLOCK_K)
# pointers
A = A + (ram[:, None] * stride_am + rk[None, :] * stride_ak)
B = B + (rk[:, None] * stride_bk + rbn[None, :] * stride_bn)
# rematerialize rm and rn to save registers
rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
rn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
w_factor = tl.load(state_w_ptr)
x_factor = tl.load(state_x_ptr + ram)[:, None]
# acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=ACC_TYPE)
acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.int32)
for k in range(0, tl.cdiv(K, BLOCK_K * SPLIT_K)):
if EVEN_K:
a = tl.load(A)
b = tl.load(B)
else:
k_remaining = K - k * (BLOCK_K * SPLIT_K)
a = tl.load(A, mask=rk[None, :] < k_remaining, other=0.)
b = tl.load(B, mask=rk[:, None] < k_remaining, other=0.)
acc += tl.dot(a, b)
A += BLOCK_K * SPLIT_K * stride_ak
B += BLOCK_K * SPLIT_K * stride_bk
acc = (w_factor * (x_factor * (acc * divfactor)))
acc = acc.to(C.dtype.element_ty)
# conditionally add bias
if has_bias:
bias = tl.load(bias + rn).to(C.dtype.element_ty)
acc = acc + bias[None, :]
C = C + (rm[:, None] * stride_cm + rn[None, :] * stride_cn)
mask = (rm < M)[:, None] & (rn < N)[None, :]
# handles write-back with reduction-splitting
if SPLIT_K == 1:
tl.store(C, acc, mask=mask)
else:
tl.atomic_add(C, acc, mask=mask)
def int8_matmul_mixed_dequanitze(a, b, state_x, state_w, bias):
device = a.device
divfactor = 1. / (127. * 127.)
has_bias = 0 if bias is None else 1
# handle non-contiguous inputs if necessary
if a.stride(0) > 1 and a.stride(1) > 1:
a = a.contiguous()
if b.stride(0) > 1 and b.stride(1) > 1:
b = b.contiguous()
# checks constraints
assert a.shape[1] == b.shape[0], "incompatible dimensions"
M, K = a.shape
_, N = b.shape
# allocates output
c = torch.empty((M, N), device=device, dtype=torch.float16)
# accumulator types
ACC_TYPE = tl.float32 #if a.dtype in [torch.float16, torch.bfloat16, torch.float32] else tl.int32
# launch int8_matmul_mixed_dequantize kernel
grid = lambda META: (triton.cdiv(M, META['BLOCK_M']) * triton.cdiv(N, META['BLOCK_N']), META['SPLIT_K'])
_int8_matmul_mixed_dequantize[grid](a, b, c, bias, state_x, state_w, M, N, K, divfactor, has_bias,
a.stride(0), a.stride(1),
b.stride(0), b.stride(1),
c.stride(0), c.stride(1),
GROUP_M=8, ACC_TYPE=ACC_TYPE)
return c
bitsandbytes/triton/int8_matmul_rowwise_dequantize.py 0 → 100644
View file @ 675baa79
import torch
from bitsandbytes.triton.triton_utils import is_triton_available
if not is_triton_available():
def int8_matmul_rowwise_dequantize(a, b, state_x, state_w, bias): return None
else:
import triton
import triton.language as tl
from triton.ops.matmul_perf_model import early_config_prune, estimate_matmul_time
# This is a matmul kernel based on triton.ops.matmul
# It is modified to support rowwise quantized input and columnwise quantized weight
# It's purpose is fused matmul then dequantize
# It does support bias.
def init_to_zero(name):
return lambda nargs: nargs[name].zero_()
def get_configs_io_bound():
configs = []
for num_stages in [2, 3, 4, 5, 6]:
for block_m in [16, 32]:
for block_k in [32, 64]:
for block_n in [32, 64, 128, 256]:
num_warps = 2 if block_n <= 64 else 4
configs.append(
triton.Config({'BLOCK_M': block_m, 'BLOCK_N': block_n, 'BLOCK_K': block_k, 'SPLIT_K': 1},
num_stages=num_stages, num_warps=num_warps))
# split_k
for split_k in [2, 4, 8, 16]:
configs.append(triton.Config({'BLOCK_M': block_m, 'BLOCK_N': block_n, 'BLOCK_K': block_k, 'SPLIT_K': split_k},
num_stages=num_stages, num_warps=num_warps, pre_hook=init_to_zero('C')))
return configs
@triton.autotune(
configs=[
# basic configs for compute-bound matmuls
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 256, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=3, num_warps=8),
triton.Config({'BLOCK_M': 256, 'BLOCK_N': 128, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=3, num_warps=8),
triton.Config({'BLOCK_M': 256, 'BLOCK_N': 64, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_M': 64, 'BLOCK_N': 256, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 128, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 64, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_M': 64, 'BLOCK_N': 128, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 32, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_M': 64, 'BLOCK_N': 32, 'BLOCK_K': 32, 'SPLIT_K': 1}, num_stages=5, num_warps=2),
# good for int8
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 256, 'BLOCK_K': 128, 'SPLIT_K': 1}, num_stages=3, num_warps=8),
triton.Config({'BLOCK_M': 256, 'BLOCK_N': 128, 'BLOCK_K': 128, 'SPLIT_K': 1}, num_stages=3, num_warps=8),
triton.Config({'BLOCK_M': 256, 'BLOCK_N': 64, 'BLOCK_K': 128, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_M': 64, 'BLOCK_N': 256, 'BLOCK_K': 128, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 128, 'BLOCK_K': 128, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 64, 'BLOCK_K': 64, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_M': 64, 'BLOCK_N': 128, 'BLOCK_K': 64, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 32, 'BLOCK_K': 64, 'SPLIT_K': 1}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_M': 64, 'BLOCK_N': 32, 'BLOCK_K': 64, 'SPLIT_K': 1}, num_stages=5, num_warps=2),
] + get_configs_io_bound(),
key=['M', 'N', 'K'],
prune_configs_by={
'early_config_prune': early_config_prune,
'perf_model': estimate_matmul_time,
'top_k': 10
},
)
@triton.heuristics({
'EVEN_K': lambda args: args['K'] % (args['BLOCK_K'] * args['SPLIT_K']) == 0,
})
@triton.jit
def _int8_matmul_rowwise_dequantize(A, B, C, bias, state_x_ptr, state_w_ptr, M, N, K, divfactor, has_bias : tl.constexpr,
stride_am, stride_ak,
stride_bk, stride_bn,
stride_cm, stride_cn,
BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr,
GROUP_M: tl.constexpr, SPLIT_K: tl.constexpr, EVEN_K: tl.constexpr,
ACC_TYPE: tl.constexpr
):
# matrix multiplication
pid = tl.program_id(0)
pid_z = tl.program_id(1)
grid_m = tl.cdiv(M, BLOCK_M)
grid_n = tl.cdiv(N, BLOCK_N)
# re-order program ID for better L2 performance
width = GROUP_M * grid_n
group_id = pid // width
group_size = min(grid_m - group_id * GROUP_M, GROUP_M)
pid_m = group_id * GROUP_M + (pid % group_size)
pid_n = (pid % width) // (group_size)
# do matrix multiplication
rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
rn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
ram = tl.max_contiguous(tl.multiple_of(rm % M, BLOCK_M), BLOCK_M)
rbn = tl.max_contiguous(tl.multiple_of(rn % N, BLOCK_N), BLOCK_N)
rk = pid_z * BLOCK_K + tl.arange(0, BLOCK_K)
# pointers
A = A + (ram[:, None] * stride_am + rk[None, :] * stride_ak)
B = B + (rk[:, None] * stride_bk + rbn[None, :] * stride_bn)
# rematerialize rm and rn to save registers
rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
rn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
w_factor = tl.load(state_w_ptr + rbn)[None, :]
x_factor = tl.load(state_x_ptr + ram)[:, None]
# acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=ACC_TYPE)
acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.int32)
for k in range(0, tl.cdiv(K, BLOCK_K * SPLIT_K)):
if EVEN_K:
a = tl.load(A)
b = tl.load(B)
else:
k_remaining = K - k * (BLOCK_K * SPLIT_K)
a = tl.load(A, mask=rk[None, :] < k_remaining, other=0.)
b = tl.load(B, mask=rk[:, None] < k_remaining, other=0.)
acc += tl.dot(a, b)
A += BLOCK_K * SPLIT_K * stride_ak
B += BLOCK_K * SPLIT_K * stride_bk
acc = (w_factor * (x_factor * (acc * divfactor)))
acc = acc.to(C.dtype.element_ty)
if has_bias:
bias = tl.load(bias + rn).to(C.dtype.element_ty)
acc = acc + bias[None, :]
C = C + (rm[:, None] * stride_cm + rn[None, :] * stride_cn)
mask = (rm < M)[:, None] & (rn < N)[None, :]
# handles write-back with reduction-splitting
if SPLIT_K == 1:
tl.store(C, acc, mask=mask)
else:
tl.atomic_add(C, acc, mask=mask)
def int8_matmul_rowwise_dequantize(a, b, state_x, state_w, bias):
divfactor = 1. / (127. * 127.)
has_bias = 0 if bias is None else 1
device = a.device
# handle non-contiguous inputs if necessary
if a.stride(0) > 1 and a.stride(1) > 1:
a = a.contiguous()
if b.stride(0) > 1 and b.stride(1) > 1:
b = b.contiguous()
# checks constraints
assert a.shape[1] == b.shape[0], "incompatible dimensions"
M, K = a.shape
_, N = b.shape
# allocates output
c = torch.empty((M, N), device=device, dtype=torch.float16)
# accumulator types
ACC_TYPE = tl.float32 #if a.dtype in [torch.float16, torch.bfloat16, torch.float32] else tl.int32
# launch int8_matmul_rowwise_dequantize kernel
grid = lambda META: (triton.cdiv(M, META['BLOCK_M']) * triton.cdiv(N, META['BLOCK_N']), META['SPLIT_K'])
_int8_matmul_rowwise_dequantize[grid](a, b, c, bias, state_x, state_w, M, N, K, divfactor, has_bias,
a.stride(0), a.stride(1),
b.stride(0), b.stride(1),
c.stride(0), c.stride(1),
GROUP_M=8, ACC_TYPE=ACC_TYPE)
return c
bitsandbytes/triton/quantize_columnwise_and_transpose.py 0 → 100644
View file @ 675baa79
import math
import torch
import time
from bitsandbytes.triton.triton_utils import is_triton_available
if not is_triton_available():
def quantize_columnwise_and_transpose(x: torch.Tensor): return None
else:
import triton
import triton.language as tl
from triton.ops.matmul_perf_model import early_config_prune, estimate_matmul_time
# This kernel does fused columnwise quantization and transpose.
# TODO: autotune this better.
@triton.autotune(
configs=[
triton.Config({}, num_stages=1),
triton.Config({}, num_stages=2),
triton.Config({}, num_stages=4),
triton.Config({}, num_stages=8),
triton.Config({}, num_stages=16),
triton.Config({}, num_stages=1, num_warps=8),
triton.Config({}, num_stages=2, num_warps=8),
triton.Config({}, num_stages=4, num_warps=8),
triton.Config({}, num_stages=8, num_warps=8),
triton.Config({}, num_stages=16, num_warps=8),
triton.Config({}, num_warps=1),
triton.Config({}, num_warps=2),
triton.Config({}, num_warps=4),
triton.Config({}, num_warps=8),
],
key=['n_elements']
)
@triton.jit
def _quantize_columnwise_and_transpose(
x_ptr,
output_ptr,
output_maxs,
n_elements,
M : tl.constexpr, N : tl.constexpr,
BLOCK_SIZE: tl.constexpr,
P2: tl.constexpr,
):
pid = tl.program_id(axis=0)
block_start = pid
p2_arange = tl.arange(0, P2)
p2_arange_mask = p2_arange < M
arange = p2_arange * N
offsets = block_start + arange
x = tl.load(x_ptr + offsets, mask=p2_arange_mask)
abs_x = tl.abs(x)
max_val = tl.max(tl.where(p2_arange_mask, abs_x, 0), axis=0)
output = tl.libdevice.llrint(127. * (x / max_val))
new_start = pid * M
new_offsets = new_start + p2_arange
tl.store(output_ptr + new_offsets, output, mask=p2_arange_mask)
tl.store(output_maxs + pid, max_val)
def quantize_columnwise_and_transpose(x: torch.Tensor):
M, N = x.shape
output = torch.empty(N, M, device=x.device, dtype=torch.int8)
output_maxs = torch.empty(x.shape[1], device=x.device, dtype=torch.float16)
P2 = int(2 ** (math.ceil(math.log2(M))))
assert x.is_cuda and output.is_cuda
n_elements = output.numel()
grid = lambda meta: (triton.cdiv(n_elements, meta['BLOCK_SIZE']),)
_quantize_columnwise_and_transpose[grid](x, output, output_maxs, n_elements, M, N, BLOCK_SIZE=M, P2=P2)
return output, output_maxs
bitsandbytes/triton/quantize_global.py 0 → 100644
View file @ 675baa79
import math
import torch
import time
from bitsandbytes.triton.triton_utils import is_triton_available
if not is_triton_available():
def quantize_global_transpose(input): return None
def quantize_global(x: torch.Tensor): return None
else:
import triton
import triton.language as tl
from triton.ops.matmul_perf_model import early_config_prune, estimate_matmul_time
# global quantize
@triton.autotune(
configs=[
triton.Config({'BLOCK_SIZE': 1024,}, num_warps=4),
triton.Config({'BLOCK_SIZE': 2048,}, num_stages=1),
],
key=['n_elements']
)
@triton.jit
def _quantize_global(
x_ptr,
absmax_inv_ptr,
output_ptr,
n_elements,
BLOCK_SIZE: tl.constexpr,
):
pid = tl.program_id(axis=0)
block_start = pid * BLOCK_SIZE
offsets = block_start + tl.arange(0, BLOCK_SIZE)
mask = offsets < n_elements
x = tl.load(x_ptr + offsets, mask=mask)
absmax_inv = tl.load(absmax_inv_ptr)
output = tl.libdevice.llrint(127. * (x * absmax_inv))
tl.store(output_ptr + offsets, output, mask=mask)
def quantize_global(x: torch.Tensor):
absmax = x.abs().max().unsqueeze(0)
absmax_inv = 1./ absmax
output = torch.empty(*x.shape, device='cuda', dtype=torch.int8)
assert x.is_cuda and output.is_cuda
n_elements = output.numel()
grid = lambda meta: (triton.cdiv(n_elements, meta['BLOCK_SIZE']),)
_quantize_global[grid](x, absmax_inv, output, n_elements)
return output, absmax
# global quantize and transpose
@triton.autotune(
configs=[
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 128, 'GROUP_M': 8}, num_warps=4),
triton.Config({'BLOCK_M': 128, 'BLOCK_N': 128, 'GROUP_M': 8}, num_warps=4),
# ...
],
key=['M', 'N']
)
@triton.jit
def _quantize_global_transpose(A, absmax_inv_ptr, B, stride_am, stride_an, stride_bn, stride_bm, M, N,
BLOCK_M : tl.constexpr,
BLOCK_N : tl.constexpr,
GROUP_M : tl.constexpr):
pid = tl.program_id(0)
grid_m = (M + BLOCK_M - 1) // BLOCK_M
grid_n = (N + BLOCK_N - 1) // BLOCK_N
width = GROUP_M * grid_n
group_id = pid // width
group_size = min(grid_m - group_id * GROUP_M, GROUP_M)
pid_m = group_id * GROUP_M + (pid % group_size)
pid_n = (pid % width) // group_size
rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
rn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
A = A + (rm[:, None] * stride_am + rn[None, :] * stride_an)
mask = (rm < M)[:, None] & (rn < N)[None, :]
a = tl.load(A, mask=mask)
absmax_inv = tl.load(absmax_inv_ptr)
# rematerialize to save registers
rm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
rn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
B = B + (rm[:, None] * stride_bm + rn[None, :] * stride_bn)
mask = (rm < M)[:, None] & (rn < N)[None, :]
output = tl.libdevice.llrint(127. * (a * absmax_inv))
tl.store(B, output, mask=mask)
def quantize_global_transpose(input):
absmax = input.abs().max().unsqueeze(0)
absmax_inv = 1./ absmax
M, N = input.shape
out = torch.empty(N, M, device='cuda', dtype=torch.int8)
assert out.size(0) == N and out.size(1) == M
assert input.stride(0) == 1 or input.stride(1) == 1
assert out.stride(0) == 1 or out.stride(1) == 1
grid = lambda META: (triton.cdiv(M, META['BLOCK_M']) * triton.cdiv(N, META['BLOCK_N']),)
_quantize_global_transpose[grid](input, absmax_inv, out, input.stride(0), input.stride(1), out.stride(0), out.stride(1), M, N)
return out, absmax
bitsandbytes/triton/quantize_rowwise.py 0 → 100644
View file @ 675baa79
import math
import torch
import time
from bitsandbytes.triton.triton_utils import is_triton_available
if not is_triton_available():
def quantize_rowwise(x: torch.Tensor): return None
else:
import triton
import triton.language as tl
from triton.ops.matmul_perf_model import early_config_prune, estimate_matmul_time
# rowwise quantize
# TODO: autotune this better.
@triton.autotune(
configs=[
triton.Config({}, num_stages=1, num_warps=8),
triton.Config({}, num_stages=2, num_warps=8),
triton.Config({}, num_stages=4, num_warps=8),
triton.Config({}, num_stages=8, num_warps=8),
triton.Config({}, num_stages=1),
triton.Config({}, num_stages=2),
triton.Config({}, num_stages=4),
triton.Config({}, num_stages=8),
triton.Config({}, num_warps=1),
triton.Config({}, num_warps=2),
triton.Config({}, num_warps=4),
triton.Config({}, num_warps=8),
],
key=['n_elements']
)
@triton.jit
def _quantize_rowwise(
x_ptr,
output_ptr,
output_maxs,
n_elements,
BLOCK_SIZE: tl.constexpr,
P2: tl.constexpr,
):
pid = tl.program_id(axis=0)
block_start = pid * BLOCK_SIZE
arange = tl.arange(0, P2)
offsets = block_start + arange
row_mask = arange < BLOCK_SIZE
x = tl.load(x_ptr + offsets, mask=row_mask)
abs_x = tl.abs(x)
max_val = tl.max(tl.where(row_mask, abs_x, 0), axis=0)
output = tl.libdevice.llrint(127. * (x / max_val))
tl.store(output_ptr + offsets, output, mask=row_mask)
tl.store(output_maxs + pid, max_val)
def quantize_rowwise(x: torch.Tensor):
output = torch.empty(*x.shape, device=x.device, dtype=torch.int8)
output_maxs = torch.empty(x.shape[0], device=x.device, dtype=torch.float16)
P2 = int(2 ** (math.ceil(math.log2(x.shape[1]))))
assert x.is_cuda and output.is_cuda
n_elements = output.numel()
grid = lambda meta: (x.shape[0],)
_quantize_rowwise[grid](x, output, output_maxs, n_elements, BLOCK_SIZE=x.shape[1], P2=P2)
return output, output_maxs
bitsandbytes/triton/triton_utils.py 0 → 100644
View file @ 675baa79
import importlib
def is_triton_available():
return importlib.util.find_spec("triton") is not None
bitsandbytes/utils.py
View file @ 675baa79
import shlex
import subprocess
import torch
from typing import Tuple
def outlier_hook(module, input):
assert isinstance(module, torch.nn.Linear)
tracer = OutlierTracer.get_instance()
hvalue = tracer.get_hvalue(module.weight)
if hvalue not in tracer.hvalue2outlier_idx:
outlier_idx = find_outlier_dims(module.weight)
tracer.outliers.append(outlier_idx)
tracer.hvalues.append(hvalue)
if len(tracer.outliers) > 1:
# assign the current layer the outlier idx found from the weight
# of the previous linear layer
if tracer.outliers[-1].numel() > 0:
assert tracer.outliers[-1].max() < module.weight.shape[1]
tracer.hvalue2outlier_idx[hvalue] = tracer.outliers[-1]
else:
# first layer, we cannot use the weight for outlier detection
# we follow a mixed approach:
# (1) zscore test of std of hidden dimension
# (2) magnitude > 6 test
merged = input[0].view(-1, input[0].shape[-1])
# (1) zscore test of std of hidden dimension
outlier_idx = find_outlier_dims(merged, reduction_dim=1, zscore=3)
# (2) magnitude > 6 test
dims = (torch.abs(input[0])> 6).sum(dim=list(range(len(input[0].shape)-1)))
outlier_idx2 = torch.where(dims > 0)[0]
outlier_idx = torch.cat([outlier_idx, outlier_idx2]).unique()
tracer.hvalue2outlier_idx[hvalue] = outlier_idx
else:
for hook in tracer.hooks:
hook.remove()
class OutlierTracer(object):
_instance = None
def __init__(self):
raise RuntimeError("Call get_instance() instead")
def initialize(self, model):
self.last_w = None
self.current_outlier_dims = None
self.hvalues = []
self.outliers = []
self.hvalue2outlier_idx = {}
self.initialized = True
self.hooks = []
for n, m in model.named_modules():
if isinstance(m, torch.nn.Linear):
self.hooks.append(m.register_forward_pre_hook(outlier_hook))
def is_initialized(self):
return getattr(self, 'initialized', False)
def get_hvalue(self, weight):
return weight.data.storage().data_ptr()
def get_outliers(self, weight):
if not self.is_initialized():
print('Outlier tracer is not initialized...')
return None
hvalue = self.get_hvalue(weight)
if hvalue in self.hvalue2outlier_idx:
return self.hvalue2outlier_idx[hvalue]
else:
return None
@classmethod
def get_instance(cls):
if cls._instance is None:
cls._instance = cls.__new__(cls)
return cls._instance
def find_outlier_dims(weight, reduction_dim=0, zscore=4.0, topk=None, rdm=False):
if rdm:
return torch.randint(0, weight.shape[1], size=(topk,), device=weight.device).long()
m = weight.mean(reduction_dim)
mm = m.mean()
mstd = m.std()
zm = (m-mm)/mstd
std = weight.std(reduction_dim)
stdm = std.mean()
stdstd = std.std()
zstd = (std-stdm)/stdstd
if topk is not None:
val, idx = torch.topk(std.abs(), k=topk, dim=0)
else:
idx = torch.where(zstd > zscore)[0]
return idx
def replace_linear(model, linear_replacement, skip_modules=["lm_head"], copy_weights=False, post_processing_function=None):
"""
Replace linear modules with a new Linear module.
Parameters:
model (`torch.nn.Module`):
Input model or `torch.nn.Module` as the function is run recursively.
linear_replacement (`torch.nn.Module`):
The linear module that replaces the old one. Only expects standard arguments.
If other arguments need to be passed, use a lambda.
skip_modules (`List[str]`, *optional*, defaults to `lm_head`):
List of modules names not to convert. Defaults to `lm_head`.
copy_weights (`bool`):
Copy the weights from the old linear module to the new one
post_processing_fun_name (`str`):
A function name of the replacement linear class that is called
after processing.
"""
for name, module in model.named_children():
if len(list(module.children())) > 0:
replace_linear(module, linear_replacement, skip_modules, copy_weights, post_processing_function)
if isinstance(module, torch.nn.Linear) and name not in skip_modules:
old_module = model._modules[name]
model._modules[name] = linear_replacement(
module.in_features,
module.out_features,
module.bias is not None,
)
if copy_weights:
model._modules[name].weight = old_module.weight
model._modules[name].bias = old_module.bias
if post_processing_function is not None:
func = getattr(module, post_processing_function, None)
if func is not None: func(module)
return model
def execute_and_return(command_string: str) -> Tuple[str, str]:
def _decode(subprocess_err_out_tuple):
......
compile_from_source.md
View file @ 675baa79
# Compiling from source
Basic steps.
1. `make [target]` where `[target]` is among `cuda92, cuda10x, cuda110, cuda11x, cpuonly`
2. `CUDA_VERSION=XXX python setup.py install`
1. `CUDA_VERSION=XXX make [target]` where `[target]` is among `cuda92, cuda10x, cuda110, cuda11x, cuda12x, cpuonly`
2. `python setup.py install`
To run these steps you will need to have the nvcc compiler installed that comes with a CUDA installation. If you use anaconda (recommended) then you can figure out which version of CUDA you are using with PyTorch via the command `conda list | grep cudatoolkit`. Then you can install the nvcc compiler by downloading and installing the same CUDA version from the [CUDA toolkit archive](https://developer.nvidia.com/cuda-toolkit-archive).
For your convenience, there is an installation script in the root directory that installs CUDA 11.1 locally and configures it automatically. After installing you should add the `bin` sub-directory to the `$PATH` variable to make the compiler visible to your system. To do this you can add this to your `.bashrc` by executing these commands:
You can install CUDA locally without sudo by following the following steps:
```bash
echo "export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/lib64/" >> ~/.bashrc
echo "export PATH=$PATH:/usr/local/cuda/bin/" >> ~/.bashrc
source ~/.bashrc
wget https://raw.githubusercontent.com/TimDettmers/bitsandbytes/main/cuda_install.sh
# Syntax cuda_install CUDA_VERSION INSTALL_PREFIX EXPORT_TO_BASH
# CUDA_VERSION in {110, 111, 112, 113, 114, 115, 116, 117, 118, 120, 121}
# EXPORT_TO_BASH in {0, 1} with 0=False and 1=True
# For example, the following installs CUDA 11.7 to ~/local/cuda-11.7 and exports the path to your .bashrc
bash cuda install 117 ~/local 1
```
By default, the Makefile will look at your `CUDA_HOME` environmental variable to find your CUDA version for compiling the library. If this path is not set it is inferred from the path of your `nvcc` compiler.
Either `nvcc` needs to be in path for the `CUDA_HOME` variable needs to be set to the CUDA directory root (e.g. `/usr/local/cuda`) in order for compilation to succeed
If you type `nvcc` and it cannot be found, you might need to add to your path or set the CUDA_HOME variable. You can run `python -m bitsandbytes` to find the path to CUDA. For example if `python -m bitsandbytes` shows you the following:
```
++++++++++++++++++ /usr/local CUDA PATHS +++++++++++++++++++
/usr/local/cuda-11.7/targets/x86_64-linux/lib/libcudart.so
```
You can set `CUDA_HOME` to `/usr/local/cuda-11.7`. For example, you might be able to compile like this.
``CUDA_HOME=~/local/cuda-11.7 CUDA_VERSION=117 make cuda11x``
If you have problems compiling the library with these instructions from source, please open an issue.
csrc/kernels.cu
View file @ 675baa79
......@@ -329,6 +329,13 @@ __device__ unsigned char dQuantizeNF4(float x)
else
return 0b0000;
}
// sign function for lion
// taken from https://stackoverflow.com/a/4609795, but not sure if there's a proper way to do this in CUDA
template <typename T> __device__ int sgn(T val)
{
return (T(0) < val) - (val < T(0));
}
template <int STOCHASTIC>
__device__ unsigned char dQuantize(float* smem_code, const float rand, float x)
......@@ -857,7 +864,6 @@ __global__ void kDequantizeBlockwise(float *code, unsigned char * A, float * abs
__syncthreads();
LoadChar(loadchar).Load(&(A[i]), qvals, valid_items_load, 128);
switch(DATA_TYPE)
{
case General8bit:
......@@ -1081,7 +1087,7 @@ template<typename T, int OPTIMIZER, int BLOCK_SIZE, int NUM_VALS>
__launch_bounds__(BLOCK_SIZE/NUM_VALS, 1)
__global__ void kPreconditionOptimizer32bit1State(T* g, T* p,
float* state1, float *unorm,
const float beta1, const float eps, const float weight_decay,
const float beta1, const float beta2, const float eps, const float weight_decay,
const int step, const float lr, const float gnorm_scale, const int n)
{
......@@ -1128,6 +1134,9 @@ __global__ void kPreconditionOptimizer32bit1State(T* g, T* p,
s1_vals[j] = s1_vals[j]*beta1 + ((float)g_vals[j]); // state update
s1_vals[j] = s1_vals[j]*s1_vals[j]; // update norm
break;
case LION:
s1_vals[j] = s1_vals[j]*beta2 + ((1.0f-beta2)*(float)g_vals[j]); // state update
break;
case RMSPROP:
s1_vals[j] = s1_vals[j]*beta1 + ((1.0f-beta1)*((float)g_vals[j])*((float)g_vals[j])); // state update
s1_vals[j] = __fdividef((float)g_vals[j],sqrtf(s1_vals[j])+eps); // update value
......@@ -1159,7 +1168,7 @@ template<typename T, int OPTIMIZER>
__launch_bounds__(TH, 1)
__global__ void kOptimizer32bit1State(T *g, T *p,
float *state1, float *unorm, const float max_unorm, const float param_norm,
const float beta1, const float eps, const float weight_decay,
const float beta1, const float beta2, const float eps, const float weight_decay,
const int step, const float lr, const float gnorm_scale, const bool skip_zeros, const int n)
{
......@@ -1228,6 +1237,10 @@ __global__ void kOptimizer32bit1State(T *g, T *p,
p_vals[j] = ((float)p_vals[j]) + update_scale*(-lr*(s1_vals[j]));
break;
case LION:
p_vals[j] = ((float)p_vals[j]) - update_scale*(lr*sgn(((float)s1_vals[j])*beta1 + ((1.0f-beta1)*((float)g_vals[j]))));
s1_vals[j] = s1_vals[j]*beta2 + ((1.0f-beta2)*((float)g_vals[j]));
break;
case RMSPROP:
s1_vals[j] = s1_vals[j]*beta1 + ((1.0f-beta1)*((float)g_vals[j])*((float)g_vals[j]));
p_vals[j] = ((float)p_vals[j]) - update_scale*(lr*__fdividef((float)g_vals[j],sqrtf((float)s1_vals[j])+eps));
......@@ -1496,7 +1509,7 @@ __global__ void
__launch_bounds__(NUM_THREADS, 2)
kPreconditionOptimizerStatic8bit1State(T* p, T* __restrict__ const g, unsigned char*__restrict__ const state1,
float *unorm,
const float beta1,
const float beta1, const float beta2,
const float eps, const int step,
float* __restrict__ const quantiles1,
float* max1, float* new_max1,
......@@ -1557,6 +1570,9 @@ kPreconditionOptimizerStatic8bit1State(T* p, T* __restrict__ const g, unsigned c
if(unorm != NULL)
local_unorm += s1_vals[j]*s1_vals[j];
break;
case LION:
s1_vals[j] = s1_vals[j]*beta2 + ((1.0f-beta2)*g_val);
break;
case RMSPROP:
s1_vals[j] = s1_vals[j]*beta1 + ((1.0f-beta1)*(g_val*g_val));
break;
......@@ -1580,9 +1596,10 @@ kPreconditionOptimizerStatic8bit1State(T* p, T* __restrict__ const g, unsigned c
template<typename T, int OPTIMIZER>
__global__ void
__launch_bounds__(1024, 1)
kOptimizerStatic8bit1State(T* p, T* const g, unsigned char* state1,
const float *unorm, const float max_unorm, const float param_norm,
const float beta1,
const float beta1, const float beta2,
const float eps, const int step, const float lr,
float* __restrict__ const quantiles1,
float* max1, float* new_max1,
......@@ -1645,8 +1662,19 @@ kOptimizerStatic8bit1State(T* p, T* const g, unsigned char* state1,
{
g_val = float(g_vals[j]);
g_val *= gnorm_scale;
if(weight_decay > 0.0f)
g_val += ((float)p_vals[j])*weight_decay;
if(weight_decay > 0.0f) {
switch(OPTIMIZER) {
case MOMENTUM:
case RMSPROP:
g_val += ((float)p_vals[j])*weight_decay;
break;
case LION:
p_vals[j] = ((float)p_vals[j])*(1.0f-lr*weight_decay);
break;
}
}
s1_vals[j] = smem_quantiles1[c1s[j]]*max1[0];
switch(OPTIMIZER)
......@@ -1659,6 +1687,10 @@ kOptimizerStatic8bit1State(T* p, T* const g, unsigned char* state1,
p_vals[j] = ((float)p_vals[j]) + (-lr*update_scale*(s1_vals[j]));
break;
case LION:
p_vals[j] = ((float)p_vals[j]) - (lr*sgn(((float)s1_vals[j])*beta1 + ((1.0f-beta1)*((float)g_val))));
s1_vals[j] = s1_vals[j]*beta2 + ((1.0f-beta2)*g_val);
break;
case RMSPROP:
s1_vals[j] = s1_vals[j]*beta1 + ((1.0f-beta1)*(g_val*g_val));
p_vals[j] = ((float)p_vals[j]) - (lr*__fdividef(g_val,sqrtf(s1_vals[j])+eps));
......@@ -1997,10 +2029,20 @@ kOptimizerStatic8bit1StateBlockwise(T* p, T* __restrict__ const g, unsigned char
{
g_val = float(g_vals[j]);
g_val *= gnorm_scale;
if(!skip_zeros || (skip_zeros && ((float)g_vals[j] != 0.0f)))
{
if(weight_decay > 0.0f)
g_val += ((float)p_vals[j])*weight_decay;
if(!skip_zeros || (skip_zeros && ((float)g_vals[j] != 0.0f)))
{
if(weight_decay > 0.0f) {
switch(OPTIMIZER) {
case MOMENTUM:
case ADAGRAD:
case RMSPROP:
g_val += ((float)p_vals[j])*weight_decay;
break;
case LION:
p_vals[j] = ((float)p_vals[j])*(1.0f-lr*weight_decay);
break;
}
}
s1_vals[j] = smem_quantiles1[lane_id][c1s[j]]*absmax1[i/BLOCK_SIZE];
......@@ -2012,6 +2054,11 @@ kOptimizerStatic8bit1StateBlockwise(T* p, T* __restrict__ const g, unsigned char
else
s1_vals[j] = (s1_vals[j]*beta1) + g_val;
break;
case LION:
// here, using gvals[j] to store the gradient smoothed by beta1 for the following parameter update, before the momentum is updated by beta2
g_vals[j] = lr*sgn(((float)s1_vals[j])*beta1 + ((1.0f-beta1)*g_val));
s1_vals[j] = s1_vals[j]*beta2 + ((1.0f-beta2)*g_val);
break;
case RMSPROP:
s1_vals[j] = s1_vals[j]*beta1 + ((1.0f-beta1)*(g_val*g_val));
break;
......@@ -2049,6 +2096,9 @@ kOptimizerStatic8bit1StateBlockwise(T* p, T* __restrict__ const g, unsigned char
case MOMENTUM:
p_vals[j] = ((float)p_vals[j]) - lr*(s1_vals[j]);
break;
case LION:
p_vals[j] = ((float)p_vals[j]) - ((float)g_vals[j]);
break;
case RMSPROP:
g_val = g_vals[j];
p_vals[j] = ((float)p_vals[j]) - lr*(__fdividef(g_val, sqrtf(s1_vals[j])+eps));
......@@ -3607,24 +3657,28 @@ template __global__ void kEstimateQuantiles(half *__restrict__ const A, float *c
#define MAKE_PreconditionOptimizer32bit1State(oname, gtype) \
template __global__ void kPreconditionOptimizer32bit1State<gtype, oname, 4096, 8>(gtype* g, gtype* p, \
float* state1, float *unorm, \
const float beta1, const float eps, const float weight_decay, \
const float beta1, const float beta2, const float eps, const float weight_decay, \
const int step, const float lr, const float gnorm_scale, const int n); \
MAKE_PreconditionOptimizer32bit1State(MOMENTUM, half)
MAKE_PreconditionOptimizer32bit1State(MOMENTUM, float)
MAKE_PreconditionOptimizer32bit1State(RMSPROP, half)
MAKE_PreconditionOptimizer32bit1State(RMSPROP, float)
MAKE_PreconditionOptimizer32bit1State(LION, half)
MAKE_PreconditionOptimizer32bit1State(LION, float)
MAKE_PreconditionOptimizer32bit1State(ADAGRAD, half)
MAKE_PreconditionOptimizer32bit1State(ADAGRAD, float)
#define MAKE_Optimizer32bit1State(oname, gtype) \
template __global__ void kOptimizer32bit1State<gtype, oname>(gtype* g, gtype* p, float* state1, float *unorm, const float max_unorm, const float param_norm, \
const float beta1, const float eps, const float weight_decay,const int step, const float lr, const float gnorm_scale, const bool skip_zeros, const int n); \
const float beta1, const float beta2, const float eps, const float weight_decay,const int step, const float lr, const float gnorm_scale, const bool skip_zeros, const int n); \
MAKE_Optimizer32bit1State(MOMENTUM, half)
MAKE_Optimizer32bit1State(MOMENTUM, float)
MAKE_Optimizer32bit1State(RMSPROP, half)
MAKE_Optimizer32bit1State(RMSPROP, float)
MAKE_Optimizer32bit1State(LION, half)
MAKE_Optimizer32bit1State(LION, float)
MAKE_Optimizer32bit1State(ADAGRAD, half)
MAKE_Optimizer32bit1State(ADAGRAD, float)
......@@ -3649,6 +3703,7 @@ template __global__ void kOptimizer32bit2State<__nv_bfloat16, ADAM>(__nv_bfloat1
template __global__ void kPreconditionOptimizerStatic8bit1State<gtype, oname>(gtype* p, gtype* __restrict__ const g, unsigned char*__restrict__ const state1, \
float *unorm, \
const float beta1, \
const float beta2, \
const float eps, const int step, \
float* __restrict__ const quantiles1, \
float* max1, float* new_max1, \
......@@ -3660,11 +3715,14 @@ MAKE_PreconditionStatic8bit1State(MOMENTUM, half)
MAKE_PreconditionStatic8bit1State(MOMENTUM, float)
MAKE_PreconditionStatic8bit1State(RMSPROP, half)
MAKE_PreconditionStatic8bit1State(RMSPROP, float)
MAKE_PreconditionStatic8bit1State(LION, half)
MAKE_PreconditionStatic8bit1State(LION, float)
#define MAKE_optimizerStatic8bit1State(oname, gtype) \
template __global__ void kOptimizerStatic8bit1State<gtype, oname>(gtype* p, gtype* const g, unsigned char* state1, \
const float *unorm, const float max_unorm, const float param_norm, \
const float beta1, \
const float beta2, \
const float eps, const int step, const float lr, \
float* __restrict__ const quantiles1, \
float* max1, float* new_max1, \
......@@ -3676,6 +3734,8 @@ MAKE_optimizerStatic8bit1State(MOMENTUM, half)
MAKE_optimizerStatic8bit1State(MOMENTUM, float)
MAKE_optimizerStatic8bit1State(RMSPROP, half)
MAKE_optimizerStatic8bit1State(RMSPROP, float)
MAKE_optimizerStatic8bit1State(LION, half)
MAKE_optimizerStatic8bit1State(LION, float)
#define MAKE_PreconditionStatic8bit2State(oname, gtype) \
template __global__ void kPreconditionOptimizerStatic8bit2State<gtype, oname>(gtype* p, gtype* __restrict__ const g, unsigned char*__restrict__ const state1, unsigned char* __restrict__ const state2, \
......@@ -3762,7 +3822,6 @@ template __global__ void kDequantizeBlockwise<float, 512, 64, 8, General8bit>(fl
template __global__ void kDequantizeBlockwise<half, 512, 64, 8, NF4>(float *code, unsigned char * A, float * absmax, half *out, const int blocksize, const int n);
template __global__ void kDequantizeBlockwise<float, 512, 64, 8, NF4>(float *code, unsigned char * A, float * absmax, float *out, const int blocksize, const int n);
#define MAKE_OptimizerStatic8bit2StateBlockwise(oname, gtype, block_size, num_per_thread) \
template __global__ void kOptimizerStatic8bit2StateBlockwise<gtype, oname, block_size, num_per_thread>(gtype* p, gtype* __restrict__ const g, unsigned char* state1, unsigned char* state2, \
const float beta1, const float beta2, \
......@@ -3791,5 +3850,7 @@ MAKE_OptimizerStatic8bit1StateBlockwise(MOMENTUM, float, 2048, 8)
MAKE_OptimizerStatic8bit1StateBlockwise(MOMENTUM, half, 2048, 8)
MAKE_OptimizerStatic8bit1StateBlockwise(RMSPROP, float, 2048, 8)
MAKE_OptimizerStatic8bit1StateBlockwise(RMSPROP, half, 2048, 8)
MAKE_OptimizerStatic8bit1StateBlockwise(LION, float, 2048, 8)
MAKE_OptimizerStatic8bit1StateBlockwise(LION, half, 2048, 8)
MAKE_OptimizerStatic8bit1StateBlockwise(ADAGRAD, float, 2048, 8)
MAKE_OptimizerStatic8bit1StateBlockwise(ADAGRAD, half, 2048, 8)
csrc/kernels.cuh
View file @ 675baa79
......@@ -34,20 +34,20 @@ __global__ void kOptimizer32bit2State(T* g, T* p,
template<typename T, int OPTIMIZER, int BLOCK_SIZE, int NUM_VALS>
__global__ void kPreconditionOptimizer32bit1State(T* g, T* p,
float* state1, float *unorm,
const float beta1, const float eps, const float weight_decay,
const float beta1, const float beta2, const float eps, const float weight_decay,
const int step, const float lr, const float gnorm_scale, const int n);
template<typename T, int OPTIMIZER>
__global__ void kOptimizer32bit1State(T* g, T* p,
float* state1, float *unorm, const float max_unorm, const float param_norm,
const float beta1, const float eps, const float weight_decay,
const float beta1, const float beta2, const float eps, const float weight_decay,
const int step, const float lr, const float gnorm_scale, const bool skip_zeros, const int n);
template<typename T, int OPTIMIZER>
__global__ void
kPreconditionOptimizerStatic8bit1State(T* p, T* __restrict__ const g, unsigned char*__restrict__ const state1,
float *unorm,
const float beta1,
const float beta1, const float beta2,
const float eps, const int step,
float* __restrict__ const quantiles1,
float* max1, float* new_max1,
......@@ -59,7 +59,7 @@ template<typename T, int OPTIMIZER>
__global__ void
kOptimizerStatic8bit1State(T* p, T* const g, unsigned char* state1,
const float *unorm, const float max_unorm, const float param_norm,
const float beta1,
const float beta1, const float beta2,
const float eps, const int step, const float lr,
float* __restrict__ const quantiles1,
float* max1, float* new_max1,
......
csrc/ops.cu
View file @ 675baa79
......@@ -54,8 +54,6 @@ template <typename T, int STOCHASTIC, int DATA_TYPE> void quantizeBlockwise(floa
{
int num_blocks = n/blocksize;
num_blocks = n % blocksize == 0 ? num_blocks : num_blocks + 1;
if(STOCHASTIC == 1)
assert(blocksize == 4096);
if(blocksize == 4096)
kQuantizeBlockwise<T, 4096, 4, STOCHASTIC, 0><<<num_blocks, 1024>>>(code, A, absmax, out, rand, rand_offset, n);
......@@ -121,17 +119,28 @@ template<typename T, int OPTIMIZER> void optimizer32bit(T* g, T* p,
case MOMENTUM:
case RMSPROP:
case ADAGRAD:
if(max_unorm > 0.0f)
{
CUDA_CHECK_RETURN(cudaMemset(unorm, 0, 1*sizeof(float)));
kPreconditionOptimizer32bit1State<T, OPTIMIZER, 4096, 8><<<num_blocks, 512>>>(g, p, state1, unorm, beta1, eps, weight_decay, step, lr, gnorm_scale, n);
kPreconditionOptimizer32bit1State<T, OPTIMIZER, 4096, 8><<<num_blocks, 512>>>(g, p, state1, unorm, beta1, beta2, eps, weight_decay, step, lr, gnorm_scale, n);
CUDA_CHECK_RETURN(cudaPeekAtLastError());
}
kOptimizer32bit1State<T, OPTIMIZER><<<num_blocks, 1024>>>(g, p, state1, unorm, max_unorm, param_norm, beta1, eps, weight_decay, step, lr, gnorm_scale, skip_zeros, n);
kOptimizer32bit1State<T, OPTIMIZER><<<num_blocks, 1024>>>(g, p, state1, unorm, max_unorm, param_norm, beta1, beta2, eps, weight_decay, step, lr, gnorm_scale, skip_zeros, n);
CUDA_CHECK_RETURN(cudaPeekAtLastError());
break;
case LION:
// in lion, the momentum update after the parameter update
kOptimizer32bit1State<T, OPTIMIZER><<<num_blocks, 1024>>>(g, p, state1, unorm, max_unorm, param_norm, beta1, beta2, eps, weight_decay, step, lr, gnorm_scale, skip_zeros, n);
CUDA_CHECK_RETURN(cudaPeekAtLastError());
if(max_unorm > 0.0f)
{
CUDA_CHECK_RETURN(cudaMemset(unorm, 0, 1*sizeof(float)));
kPreconditionOptimizer32bit1State<T, OPTIMIZER, 4096, 8><<<num_blocks, 512>>>(g, p, state1, unorm, beta1, beta2, eps, weight_decay, step, lr, gnorm_scale, n);
CUDA_CHECK_RETURN(cudaPeekAtLastError());
}
break;
}
}
......@@ -165,12 +174,22 @@ template<typename T, int OPTIMIZER> void optimizerStatic8bit(T* p, T* g,
case RMSPROP:
case ADAGRAD:
CUDA_CHECK_RETURN(cudaMemset(new_max1, 0, 1*sizeof(float)));
kPreconditionOptimizerStatic8bit1State<T, OPTIMIZER><<<num_blocks, 256>>>(p, g, state1, unorm, beta1, eps, step, quantiles1, max1, new_max1, weight_decay, gnorm_scale, n);
kPreconditionOptimizerStatic8bit1State<T, OPTIMIZER><<<num_blocks, 256>>>(p, g, state1, unorm, beta1, beta2, eps, step, quantiles1, max1, new_max1, weight_decay, gnorm_scale, n);
CUDA_CHECK_RETURN(cudaPeekAtLastError());
kOptimizerStatic8bit1State<T, OPTIMIZER><<<num_blocks, 1024>>>(p, g, state1, unorm, max_unorm, param_norm, beta1, eps, step, lr,
kOptimizerStatic8bit1State<T, OPTIMIZER><<<num_blocks, 1024>>>(p, g, state1, unorm, max_unorm, param_norm, beta1, beta2, eps, step, lr,
quantiles1, max1, new_max1, weight_decay, gnorm_scale, n);
CUDA_CHECK_RETURN(cudaPeekAtLastError());
break;
case LION:
// in lion, the momentum update happens after the parameter update
kOptimizerStatic8bit1State<T, OPTIMIZER><<<num_blocks, 1024>>>(p, g, state1, unorm, max_unorm, param_norm, beta1, beta2, eps, step, lr,
quantiles1, max1, new_max1, weight_decay, gnorm_scale, n);
CUDA_CHECK_RETURN(cudaPeekAtLastError());
CUDA_CHECK_RETURN(cudaMemset(new_max1, 0, 1*sizeof(float)));
kPreconditionOptimizerStatic8bit1State<T, OPTIMIZER><<<num_blocks, 256>>>(p, g, state1, unorm, beta1, beta2, eps, step, quantiles1, max1, new_max1, weight_decay, gnorm_scale, n);
CUDA_CHECK_RETURN(cudaPeekAtLastError());
break;
default:
break;
}
......@@ -199,6 +218,7 @@ template<typename T, int OPTIMIZER> void optimizerStatic8bitBlockwise(T* p, T* g
case MOMENTUM:
case RMSPROP:
case ADAGRAD:
case LION:
num_blocks = n/BLOCKSIZE_1STATE;
num_blocks = n % BLOCKSIZE_1STATE == 0 ? num_blocks : num_blocks + 1;
kOptimizerStatic8bit1StateBlockwise<T, OPTIMIZER, BLOCKSIZE_1STATE, NUM_1STATE><<<num_blocks, BLOCKSIZE_1STATE/NUM_1STATE>>>(p, g, state1, beta1, beta2, eps, step, lr,
......@@ -780,6 +800,8 @@ MAKE_optimizer32bit(MOMENTUM, half)
MAKE_optimizer32bit(MOMENTUM, float)
MAKE_optimizer32bit(RMSPROP, half)
MAKE_optimizer32bit(RMSPROP, float)
MAKE_optimizer32bit(LION, half)
MAKE_optimizer32bit(LION, float)
MAKE_optimizer32bit(ADAGRAD, half)
MAKE_optimizer32bit(ADAGRAD, float)
......@@ -799,6 +821,8 @@ MAKE_optimizerStatic8bit(MOMENTUM, half)
MAKE_optimizerStatic8bit(MOMENTUM, float)
MAKE_optimizerStatic8bit(RMSPROP, half)
MAKE_optimizerStatic8bit(RMSPROP, float)
MAKE_optimizerStatic8bit(LION, half)
MAKE_optimizerStatic8bit(LION, float)
#define MAKE_optimizerStatic8bitBlockwise(gtype, optim_name) \
template void optimizerStatic8bitBlockwise<gtype, optim_name>(gtype* p, gtype* g, \
......@@ -811,6 +835,8 @@ MAKE_optimizerStatic8bitBlockwise(half, MOMENTUM);
MAKE_optimizerStatic8bitBlockwise(float, MOMENTUM);
MAKE_optimizerStatic8bitBlockwise(half, RMSPROP);
MAKE_optimizerStatic8bitBlockwise(float, RMSPROP);
MAKE_optimizerStatic8bitBlockwise(half, LION);
MAKE_optimizerStatic8bitBlockwise(float, LION);
MAKE_optimizerStatic8bitBlockwise(half, ADAGRAD);
MAKE_optimizerStatic8bitBlockwise(float, ADAGRAD);
......
csrc/ops.cuh
View file @ 675baa79
......@@ -75,6 +75,7 @@ typedef enum Optimizer_t
RMSPROP = 2,
LARS = 3,
ADAGRAD = 4,
LION = 5,
} Optimizer_t;
typedef enum Transform_t
......
csrc/pythonInterface.c
View file @ 675baa79
......@@ -38,7 +38,7 @@ MAKE_ELEMENTWISE_FUNC(_mul, fp32, float, _MUL)
#define MAKE_FUNC32(fname, oname, gtype, gbits) \
void fname##32bit_g##gbits(gtype *g, gtype *p, \
void fname##32bit_grad_##gbits(gtype *g, gtype *p, \
float* state1, float* state2, float *unorm, float max_unorm, float param_norm, \
const float beta1, const float beta2, const float eps, const float weight_decay, \
const int step, const float lr, float gnorm_scale, bool skip_zeros, const int n) \
......@@ -51,11 +51,13 @@ MAKE_FUNC32(adam, ADAM, half, fp16)
MAKE_FUNC32(adam, ADAM, __nv_bfloat16, bf16)
MAKE_FUNC32(rmsprop, RMSPROP, float, 32)
MAKE_FUNC32(rmsprop, RMSPROP, half, 16)
MAKE_FUNC32(lion, LION, float, 32)
MAKE_FUNC32(lion, LION, half, 16)
MAKE_FUNC32(adagrad, ADAGRAD, float, 32)
MAKE_FUNC32(adagrad, ADAGRAD, half, 16)
#define MAKE_FUNC8(fname, oname, gtype, gbits) \
void fname##_static_8bit_g##gbits(gtype* p, gtype* g, unsigned char* state1, unsigned char* state2, \
void fname##_static_8bit_grad_##gbits(gtype* p, gtype* g, unsigned char* state1, unsigned char* state2, \
float *unorm, float max_unorm, float param_norm, \
float beta1, float beta2, \
float eps, int step, float lr, \
......@@ -73,9 +75,11 @@ MAKE_FUNC8(momentum, MOMENTUM, float, 32)
MAKE_FUNC8(momentum, MOMENTUM, half, 16)
MAKE_FUNC8(rmsprop, RMSPROP, float, 32)
MAKE_FUNC8(rmsprop, RMSPROP, half, 16)
MAKE_FUNC8(lion, LION, float, 32)
MAKE_FUNC8(lion, LION, half, 16)
#define MAKE_BLOCKWISE8(fname, optim_name, gtype, gbits) \
void fname##_8bit_blockwise_##gbits(gtype* p, gtype* g, \
void fname##_8bit_blockwise_grad_##gbits(gtype* p, gtype* g, \
unsigned char* state1, unsigned char* state2, float beta1, float beta2, float eps, int step, float lr, \
float* quantiles1, float* quantiles2, float* absmax1, float* absmax2, float weight_decay, const float gnorm_scale, bool skip_zeros, int n)\
{ optimizerStatic8bitBlockwise<gtype, optim_name>(p, g, state1, state2, beta1, beta2, eps, step, lr, quantiles1, quantiles2, absmax1, absmax2, weight_decay, gnorm_scale, skip_zeros, n); }\
......@@ -89,6 +93,8 @@ MAKE_BLOCKWISE8(rmsprop, RMSPROP, float, fp32)
MAKE_BLOCKWISE8(adagrad, ADAGRAD, half, fp16)
MAKE_BLOCKWISE8(adagrad, ADAGRAD, float, fp32)
MAKE_BLOCKWISE8(adam, ADAM, __nv_bfloat16, bf16)
MAKE_BLOCKWISE8(lion, LION, half, fp16)
MAKE_BLOCKWISE8(lion, LION, float, fp32)
void percentileClipping_g32(float * g, float *gnorm_vec, int step, const int n){ percentileClipping<float>(g, gnorm_vec, step, n); }
......@@ -96,8 +102,6 @@ void percentileClipping_g16(half * g, float *gnorm_vec, int step, const int n){
void quantizeBlockwise_fp16(float * code, half *A, float *absmax, unsigned char *out, int blocksize, const int n){ quantizeBlockwise<half, 0, General8bit>(code, A, absmax, out, NULL, 0, blocksize, n); }
void quantizeBlockwise_fp32(float * code, float *A, float *absmax, unsigned char *out, int blocksize, const int n){ quantizeBlockwise<float, 0, General8bit>(code, A, absmax, out, NULL, 0, blocksize, n); }
void quantizeBlockwise_stochastic_fp16(float * code, half *A, float *absmax, unsigned char *out, float* rand, int rand_offset, const int n){ quantizeBlockwise<half, 1, General8bit>(code, A, absmax, out, rand, rand_offset, 4096, n); }
void quantizeBlockwise_stochastic_fp32(float * code, float *A, float *absmax, unsigned char *out, float* rand, int rand_offset, const int n){ quantizeBlockwise<float, 1, General8bit>(code, A, absmax, out, rand, rand_offset, 4096, n); }
void quantizeBlockwise_fp16_fp4(float * code, half *A, float *absmax, unsigned char *out, int blocksize, const int n){ quantizeBlockwise<half, 0, FP4>(NULL, A, absmax, out, NULL, 0, blocksize, n); }
void quantizeBlockwise_fp32_fp4(float * code, float *A, float *absmax, unsigned char *out, int blocksize, const int n){ quantizeBlockwise<float, 0, FP4>(NULL, A, absmax, out, NULL, 0, blocksize, n); }
void quantizeBlockwise_fp16_nf4(float * code, half *A, float *absmax, unsigned char *out, int blocksize, const int n){ quantizeBlockwise<half, 0, NF4>(NULL, A, absmax, out, NULL, 0, blocksize, n); }
......@@ -110,6 +114,7 @@ void dequantizeBlockwise_fp32_fp4(float *code, unsigned char *A, float *absmax,
void dequantizeBlockwise_fp16_nf4(float *code, unsigned char *A, float *absmax, half *out, int blocksize, const int n){ dequantizeBlockwise<half, NF4>(NULL, A, absmax, out, blocksize, n); } \
void dequantizeBlockwise_fp32_nf4(float *code, unsigned char *A, float *absmax, float *out, int blocksize, const int n){ dequantizeBlockwise<float, NF4>(NULL, A, absmax, out, blocksize, n); }
#define MAKE_FUNC_TRANSFORM(fbits, fsrc, ftrgt, ftranspose, dtype, src, target, transpose, bits) \
void transform_##fbits##_##fsrc##_to_##ftrgt##_##ftranspose(cublasLtHandle_t ltHandle, dtype *A, dtype *out, int dim1, int dim2) \
{ \
......@@ -169,8 +174,6 @@ extern "C"
void cdequantize(float *code, unsigned char *A, float *out, int n){ dequantize(code, A, out, n); }
void cquantize_blockwise_fp16(float * code, half *A, float *absmax, unsigned char *out, int blocksize, const int n){ quantizeBlockwise_fp16(code, A, absmax, out, blocksize, n); }
void cquantize_blockwise_fp32(float * code, float *A, float *absmax, unsigned char *out, int blocksize, const int n){ quantizeBlockwise_fp32(code, A, absmax, out, blocksize, n); }
void cquantize_blockwise_stochastic_fp16(float * code, half *A, float *absmax, unsigned char *out, float *rand, int rand_offset, const int n){ quantizeBlockwise_stochastic_fp16(code, A, absmax, out, rand, rand_offset, n); }
void cquantize_blockwise_stochastic_fp32(float * code, float *A, float *absmax, unsigned char *out, float *rand, int rand_offset, const int n){ quantizeBlockwise_stochastic_fp32(code, A, absmax, out, rand, rand_offset, n); }
void cdequantize_blockwise_fp16(float *code, unsigned char *A, float *absmax, half *out, int blocksize, const int n){ dequantizeBlockwise_fp16(code, A, absmax, out, blocksize, n); }
void cdequantize_blockwise_fp32(float *code, unsigned char *A, float *absmax, float *out, int blocksize, const int n){ dequantizeBlockwise_fp32(code, A, absmax, out, blocksize, n); }
......@@ -185,11 +188,11 @@ extern "C"
void cdequantize_blockwise_fp32_nf4(float *code, unsigned char *A, float *absmax, float *out, int blocksize, const int n){ dequantizeBlockwise_fp32_nf4(code, A, absmax, out, blocksize, n); }
#define MAKE_CFUNC32(name, gtype, gbits) \
void c##name##32bit_g##gbits(gtype *g, gtype *p, \
void c##name##32bit_grad_##gbits(gtype *g, gtype *p, \
float* state1, float* state2, float *unorm, float max_unorm, float param_norm, \
const float beta1, const float beta2, const float eps, const float weight_decay, \
const int step, const float lr, const float gnorm_scale, bool skip_zeros, const int n) \
{ name##32bit_g##gbits(g, p, state1, state2, unorm, max_unorm, param_norm, beta1, beta2, eps, weight_decay, step, lr, gnorm_scale, skip_zeros, n); } \
{ name##32bit_grad_##gbits(g, p, state1, state2, unorm, max_unorm, param_norm, beta1, beta2, eps, weight_decay, step, lr, gnorm_scale, skip_zeros, n); } \
MAKE_CFUNC32(adam, float, fp32)
MAKE_CFUNC32(adam, half, fp16)
......@@ -198,11 +201,13 @@ extern "C"
MAKE_CFUNC32(momentum, half, 16)
MAKE_CFUNC32(rmsprop, float, 32)
MAKE_CFUNC32(rmsprop, half, 16)
MAKE_CFUNC32(lion, float, 32)
MAKE_CFUNC32(lion, half, 16)
MAKE_CFUNC32(adagrad, float, 32)
MAKE_CFUNC32(adagrad, half, 16)
#define MAKE_CFUNC8(name, gtype, gbits) \
void c##name##_static_8bit_g##gbits(gtype* p, gtype* g, unsigned char* state1, unsigned char* state2, \
void c##name##_static_8bit_grad_##gbits(gtype* p, gtype* g, unsigned char* state1, unsigned char* state2, \
float *unorm, float max_unorm, float param_norm, \
float beta1, float beta2, \
float eps, int step, float lr, \
......@@ -210,7 +215,7 @@ extern "C"
float* max1, float* max2, float* new_max1, float* new_max2, \
float weight_decay, float gnorm_scale, int n) \
{ \
name##_static_8bit_g##gbits(g, p, state1, state2, unorm, max_unorm, param_norm, beta1, beta2, eps, step, lr, \
name##_static_8bit_grad_##gbits(g, p, state1, state2, unorm, max_unorm, param_norm, beta1, beta2, eps, step, lr, \
quantiles1, quantiles2, max1, max2, new_max1, new_max2, weight_decay, gnorm_scale, n); \
} \
......@@ -220,12 +225,14 @@ extern "C"
MAKE_CFUNC8(momentum, half, 16)
MAKE_CFUNC8(rmsprop, float, 32)
MAKE_CFUNC8(rmsprop, half, 16)
MAKE_CFUNC8(lion, float, 32)
MAKE_CFUNC8(lion, half, 16)
#define MAKE_CBLOCKWISE8(fname, optim_name, gtype, gbits) \
void c##fname##_8bit_blockwise_##gbits(gtype* p, gtype* g, \
void c##fname##_8bit_blockwise_grad_##gbits(gtype* p, gtype* g, \
unsigned char* state1, unsigned char* state2, float beta1, float beta2, float eps, int step, float lr, \
float* quantiles1, float* quantiles2, float* absmax1, float* absmax2, float weight_decay, const float gnorm_scale, bool skip_zeros, int n) \
{ fname##_8bit_blockwise_##gbits(p, g, state1, state2, beta1, beta2, eps, step, lr, quantiles1, quantiles2, absmax1, absmax2, weight_decay, gnorm_scale, skip_zeros, n); } \
{ fname##_8bit_blockwise_grad_##gbits(p, g, state1, state2, beta1, beta2, eps, step, lr, quantiles1, quantiles2, absmax1, absmax2, weight_decay, gnorm_scale, skip_zeros, n); } \
MAKE_CBLOCKWISE8(adam, ADAM, half, fp16)
MAKE_CBLOCKWISE8(adam, ADAM, float, fp32)
......@@ -236,6 +243,8 @@ extern "C"
MAKE_CBLOCKWISE8(adagrad, ADAGRAD, half, fp16)
MAKE_CBLOCKWISE8(adagrad, ADAGRAD, float, fp32)
MAKE_CBLOCKWISE8(adam, ADAM, __nv_bfloat16, bf16)
MAKE_CBLOCKWISE8(lion, LION, half, fp16)
MAKE_CBLOCKWISE8(lion, LION, float, fp32)
void cpercentile_clipping_g32(float * g, float *gnorm_vec, int step, const int n){ percentileClipping_g32(g, gnorm_vec, step, n); }
void cpercentile_clipping_g16(half * g, float *gnorm_vec, int step, const int n){ percentileClipping_g16(g, gnorm_vec, step, n); }
......
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment